Simultaneous tomographic reconstruction and segmentation with class priors

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We consider tomographic imaging problems where the goal is to obtain both a reconstructed image and a corresponding segmentation. A classical approach is to first reconstruct and then segment the image; more recent approaches use a discrete tomography approach where reconstruction and segmentation are combined to produce a reconstruction that is identical to the segmentation. We consider instead a hybrid approach that simultaneously produces both a reconstructed image and segmentation. We incorporate priors about the desired classes of the segmentation through a Hidden Markov Measure Field Model, and we impose a regularization term for the spatial variation of the classes across neighbouring pixels. We also present an efficient implementation of our algorithm based on state-of-the-art numerical optimization algorithms. Simulation experiments with artificial and real data demonstrate that our combined approach can produce better results than the classical two-step approach.
Original languageEnglish
JournalInverse Problems in Science and Engineering
Issue number8
Pages (from-to)1432-1453
Publication statusPublished - 2015


  • Tomographic reconstruction
  • Segmentation
  • Regularization
  • Numerical optimization
  • Hidden Markov Measure Field Models


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